Overview

Dataset statistics

Number of variables27
Number of observations43
Missing cells87
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory121.1 B

Variable types

DateTime1
Categorical3
Numeric23

Alerts

Mood_num is highly correlated with Mood_bin_num and 1 other fieldsHigh correlation
Mood_bin_num is highly correlated with Mood_num and 1 other fieldsHigh correlation
Friends is highly correlated with Mood_num and 1 other fieldsHigh correlation
Sleepiness is highly correlated with Sleepiness7DayRollingMeanHigh correlation
Sleepiness7DayRollingMean is highly correlated with SleepinessHigh correlation
Mood_num is highly correlated with Mood_bin_num and 1 other fieldsHigh correlation
Mood_bin_num is highly correlated with Mood_num and 1 other fieldsHigh correlation
Friends is highly correlated with Mood_num and 1 other fieldsHigh correlation
NightOut is highly correlated with AlcoholDrinksHigh correlation
AlcoholDrinks is highly correlated with NightOutHigh correlation
Sleepiness is highly correlated with Sleepiness7DayRollingMeanHigh correlation
Sleepiness7DayRollingMean is highly correlated with SleepinessHigh correlation
Mood_num is highly correlated with Mood_bin_num and 1 other fieldsHigh correlation
Mood_bin_num is highly correlated with Mood_num and 1 other fieldsHigh correlation
Friends is highly correlated with Mood_num and 1 other fieldsHigh correlation
Mood is highly correlated with Mood_binHigh correlation
Mood_bin is highly correlated with MoodHigh correlation
Date is highly correlated with Mood and 25 other fieldsHigh correlation
Mood is highly correlated with Date and 5 other fieldsHigh correlation
Mood_num is highly correlated with Date and 5 other fieldsHigh correlation
Mood_bin is highly correlated with Date and 4 other fieldsHigh correlation
Mood_bin_num is highly correlated with Date and 4 other fieldsHigh correlation
Time is highly correlated with Date and 5 other fieldsHigh correlation
Day is highly correlated with Date and 1 other fieldsHigh correlation
Entertainment is highly correlated with Date and 1 other fieldsHigh correlation
Exercise is highly correlated with DateHigh correlation
Family is highly correlated with Date and 1 other fieldsHigh correlation
Food is highly correlated with DateHigh correlation
Friends is highly correlated with Date and 5 other fieldsHigh correlation
Hobby is highly correlated with Date and 2 other fieldsHigh correlation
Love is highly correlated with DateHigh correlation
NightOut is highly correlated with Date and 2 other fieldsHigh correlation
Projects is highly correlated with DateHigh correlation
School is highly correlated with Date and 1 other fieldsHigh correlation
SelfCare is highly correlated with DateHigh correlation
Sleep is highly correlated with Date and 2 other fieldsHigh correlation
CaffeineCups is highly correlated with Date and 1 other fieldsHigh correlation
AlcoholDrinks is highly correlated with Date and 3 other fieldsHigh correlation
Stress is highly correlated with Date and 2 other fieldsHigh correlation
Sleepiness is highly correlated with Date and 5 other fieldsHigh correlation
PreviousNightMood is highly correlated with Date and 2 other fieldsHigh correlation
Stress7DayRollingMean is highly correlated with Date and 7 other fieldsHigh correlation
Sleepiness7DayRollingMean is highly correlated with Date and 4 other fieldsHigh correlation
AtHome is highly correlated with Date and 2 other fieldsHigh correlation
CaffeineCups has 11 (25.6%) missing values Missing
AlcoholDrinks has 11 (25.6%) missing values Missing
Stress has 11 (25.6%) missing values Missing
Sleepiness has 11 (25.6%) missing values Missing
PreviousNightMood has 20 (46.5%) missing values Missing
Stress7DayRollingMean has 11 (25.6%) missing values Missing
Sleepiness7DayRollingMean has 11 (25.6%) missing values Missing
AtHome has 1 (2.3%) missing values Missing
Day is uniformly distributed Uniform
Date has unique values Unique
Mood_num has 3 (7.0%) zeros Zeros
Mood_bin_num has 17 (39.5%) zeros Zeros
Entertainment has 42 (97.7%) zeros Zeros
Exercise has 33 (76.7%) zeros Zeros
Family has 33 (76.7%) zeros Zeros
Food has 37 (86.0%) zeros Zeros
Friends has 22 (51.2%) zeros Zeros
Hobby has 40 (93.0%) zeros Zeros
Love has 34 (79.1%) zeros Zeros
NightOut has 41 (95.3%) zeros Zeros
Projects has 31 (72.1%) zeros Zeros
School has 16 (37.2%) zeros Zeros
SelfCare has 40 (93.0%) zeros Zeros
Sleep has 37 (86.0%) zeros Zeros
CaffeineCups has 28 (65.1%) zeros Zeros
AlcoholDrinks has 28 (65.1%) zeros Zeros
Stress has 2 (4.7%) zeros Zeros
AtHome has 35 (81.4%) zeros Zeros

Reproduction

Analysis started2022-11-25 21:27:39.100407
Analysis finished2022-11-25 21:28:54.233607
Duration1 minute and 15.13 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Date

HIGH CORRELATION
UNIQUE

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size688.0 B
Minimum2022-09-30 00:00:00
Maximum2022-11-11 00:00:00
2022-11-25T21:28:54.370187image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:54.530150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)

Mood
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size599.0 B
Good
20 
Okay
10 
Terrific
Bad
Terrible

Length

Max length8
Median length4
Mean length4.744186047
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowOkay
3rd rowBad
4th rowTerrific
5th rowTerrific

Common Values

ValueCountFrequency (%)
Good20
46.5%
Okay10
23.3%
Terrific6
 
14.0%
Bad4
 
9.3%
Terrible3
 
7.0%

Length

2022-11-25T21:28:54.710393image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-25T21:28:54.800582image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
good20
46.5%
okay10
23.3%
terrific6
 
14.0%
bad4
 
9.3%
terrible3
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mood_num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.511627907
Minimum0
Maximum4
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:54.900366image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q12
median3
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.077300264
Coefficient of variation (CV)0.428925105
Kurtosis0.2712862386
Mean2.511627907
Median Absolute Deviation (MAD)1
Skewness-0.8103792536
Sum108
Variance1.160575858
MonotonicityNot monotonic
2022-11-25T21:28:55.050170image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
320
46.5%
210
23.3%
46
 
14.0%
14
 
9.3%
03
 
7.0%
ValueCountFrequency (%)
03
 
7.0%
14
 
9.3%
210
23.3%
320
46.5%
46
 
14.0%
ValueCountFrequency (%)
46
 
14.0%
320
46.5%
210
23.3%
14
 
9.3%
03
 
7.0%

Mood_bin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size511.0 B
Good
26 
Bad
17 

Length

Max length4
Median length4
Mean length3.604651163
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowBad
3rd rowBad
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Good26
60.5%
Bad17
39.5%

Length

2022-11-25T21:28:55.225903image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-25T21:28:55.345327image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
good26
60.5%
bad17
39.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mood_bin_num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6046511628
Minimum0
Maximum1
Zeros17
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:55.989326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4947117912
Coefficient of variation (CV)0.8181771932
Kurtosis-1.893499614
Mean0.6046511628
Median Absolute Deviation (MAD)0
Skewness-0.44371733
Sum26
Variance0.2447397564
MonotonicityNot monotonic
2022-11-25T21:28:56.120535image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
126
60.5%
017
39.5%
ValueCountFrequency (%)
017
39.5%
126
60.5%
ValueCountFrequency (%)
126
60.5%
017
39.5%

Time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.12906977
Minimum12.98333333
Maximum23.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:28:56.267450image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12.98333333
5-th percentile15.74
Q116.075
median16.81666667
Q320.03333333
95-th percentile22.91166667
Maximum23.25
Range10.26666667
Interquartile range (IQR)3.958333333

Descriptive statistics

Standard deviation2.829060955
Coefficient of variation (CV)0.1560510821
Kurtosis-0.7596181064
Mean18.12906977
Median Absolute Deviation (MAD)1.016666667
Skewness0.5909996437
Sum779.55
Variance8.003585887
MonotonicityNot monotonic
2022-11-25T21:28:56.442541image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
164
 
9.3%
16.066666672
 
4.7%
16.11
 
2.3%
23.183333331
 
2.3%
18.81
 
2.3%
18.733333331
 
2.3%
15.81
 
2.3%
15.733333331
 
2.3%
16.283333331
 
2.3%
17.816666671
 
2.3%
Other values (29)29
67.4%
ValueCountFrequency (%)
12.983333331
 
2.3%
13.533333331
 
2.3%
15.733333331
 
2.3%
15.81
 
2.3%
15.933333331
 
2.3%
164
9.3%
16.066666672
4.7%
16.083333331
 
2.3%
16.11
 
2.3%
16.116666671
 
2.3%
ValueCountFrequency (%)
23.251
2.3%
23.183333331
2.3%
22.916666671
2.3%
22.866666671
2.3%
22.766666671
2.3%
22.733333331
2.3%
22.716666671
2.3%
22.151
2.3%
21.751
2.3%
211
2.3%

Day
Categorical

HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size743.0 B
Friday
Monday
Tuesday
Wednesday
Thursday
Other values (2)
12 

Length

Max length9
Median length7
Mean length7.11627907
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowSaturday
3rd rowSunday
4th rowMonday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Friday7
16.3%
Monday6
14.0%
Tuesday6
14.0%
Wednesday6
14.0%
Thursday6
14.0%
Saturday6
14.0%
Sunday6
14.0%

Length

2022-11-25T21:28:56.595054image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-25T21:28:56.719215image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
friday7
16.3%
monday6
14.0%
tuesday6
14.0%
wednesday6
14.0%
thursday6
14.0%
saturday6
14.0%
sunday6
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Entertainment
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02325581395
Minimum0
Maximum1
Zeros42
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:56.840109image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1524985703
Coefficient of variation (CV)6.557438524
Kurtosis43
Mean0.02325581395
Median Absolute Deviation (MAD)0
Skewness6.557438524
Sum1
Variance0.02325581395
MonotonicityNot monotonic
2022-11-25T21:28:56.975014image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
042
97.7%
11
 
2.3%
ValueCountFrequency (%)
042
97.7%
11
 
2.3%
ValueCountFrequency (%)
11
 
2.3%
042
97.7%

Exercise
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2325581395
Minimum0
Maximum1
Zeros33
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:57.100220image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4274625744
Coefficient of variation (CV)1.83808907
Kurtosis-0.2936585366
Mean0.2325581395
Median Absolute Deviation (MAD)0
Skewness1.312338538
Sum10
Variance0.1827242525
MonotonicityNot monotonic
2022-11-25T21:28:57.225409image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
033
76.7%
110
 
23.3%
ValueCountFrequency (%)
033
76.7%
110
 
23.3%
ValueCountFrequency (%)
110
 
23.3%
033
76.7%

Family
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2325581395
Minimum0
Maximum1
Zeros33
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:57.345125image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4274625744
Coefficient of variation (CV)1.83808907
Kurtosis-0.2936585366
Mean0.2325581395
Median Absolute Deviation (MAD)0
Skewness1.312338538
Sum10
Variance0.1827242525
MonotonicityNot monotonic
2022-11-25T21:28:57.470505image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
033
76.7%
110
 
23.3%
ValueCountFrequency (%)
033
76.7%
110
 
23.3%
ValueCountFrequency (%)
110
 
23.3%
033
76.7%

Food
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1395348837
Minimum0
Maximum1
Zeros37
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:57.585173image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3506046036
Coefficient of variation (CV)2.512666326
Kurtosis2.777851022
Mean0.1395348837
Median Absolute Deviation (MAD)0
Skewness2.156553432
Sum6
Variance0.122923588
MonotonicityNot monotonic
2022-11-25T21:28:57.700360image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
037
86.0%
16
 
14.0%
ValueCountFrequency (%)
037
86.0%
16
 
14.0%
ValueCountFrequency (%)
16
 
14.0%
037
86.0%

Friends
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.488372093
Minimum0
Maximum1
Zeros22
Zeros (%)51.2%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:57.820165image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5057805389
Coefficient of variation (CV)1.035645865
Kurtosis-2.097560976
Mean0.488372093
Median Absolute Deviation (MAD)0
Skewness0.04822297821
Sum21
Variance0.2558139535
MonotonicityNot monotonic
2022-11-25T21:28:57.950337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
022
51.2%
121
48.8%
ValueCountFrequency (%)
022
51.2%
121
48.8%
ValueCountFrequency (%)
121
48.8%
022
51.2%

Hobby
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06976744186
Minimum0
Maximum1
Zeros40
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:58.075515image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.9
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2577696311
Coefficient of variation (CV)3.694698046
Kurtosis10.7552439
Mean0.06976744186
Median Absolute Deviation (MAD)0
Skewness3.500951685
Sum3
Variance0.06644518272
MonotonicityNot monotonic
2022-11-25T21:28:58.190479image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
040
93.0%
13
 
7.0%
ValueCountFrequency (%)
040
93.0%
13
 
7.0%
ValueCountFrequency (%)
13
 
7.0%
040
93.0%

Love
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2093023256
Minimum0
Maximum1
Zeros34
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:58.310512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4116250915
Coefficient of variation (CV)1.966653215
Kurtosis0.2015303682
Mean0.2093023256
Median Absolute Deviation (MAD)0
Skewness1.481338505
Sum9
Variance0.1694352159
MonotonicityNot monotonic
2022-11-25T21:28:58.435482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
034
79.1%
19
 
20.9%
ValueCountFrequency (%)
034
79.1%
19
 
20.9%
ValueCountFrequency (%)
19
 
20.9%
034
79.1%

NightOut
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04651162791
Minimum0
Maximum1
Zeros41
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:58.570352image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2130826323
Coefficient of variation (CV)4.581276595
Kurtosis18.80130874
Mean0.04651162791
Median Absolute Deviation (MAD)0
Skewness4.464087485
Sum2
Variance0.04540420819
MonotonicityNot monotonic
2022-11-25T21:28:58.690356image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
041
95.3%
12
 
4.7%
ValueCountFrequency (%)
041
95.3%
12
 
4.7%
ValueCountFrequency (%)
12
 
4.7%
041
95.3%

Projects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2790697674
Minimum0
Maximum1
Zeros31
Zeros (%)72.1%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:58.805177image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4538502688
Coefficient of variation (CV)1.626296796
Kurtosis-1.006490952
Mean0.2790697674
Median Absolute Deviation (MAD)0
Skewness1.021073834
Sum12
Variance0.2059800664
MonotonicityNot monotonic
2022-11-25T21:28:58.915015image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
031
72.1%
112
 
27.9%
ValueCountFrequency (%)
031
72.1%
112
 
27.9%
ValueCountFrequency (%)
112
 
27.9%
031
72.1%

School
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6279069767
Minimum0
Maximum1
Zeros16
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:59.030028image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4890834876
Coefficient of variation (CV)0.7789107395
Kurtosis-1.784383469
Mean0.6279069767
Median Absolute Deviation (MAD)0
Skewness-0.5485621366
Sum27
Variance0.2392026578
MonotonicityNot monotonic
2022-11-25T21:28:59.145574image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
127
62.8%
016
37.2%
ValueCountFrequency (%)
016
37.2%
127
62.8%
ValueCountFrequency (%)
127
62.8%
016
37.2%

SelfCare
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06976744186
Minimum0
Maximum1
Zeros40
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:59.255053image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.9
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2577696311
Coefficient of variation (CV)3.694698046
Kurtosis10.7552439
Mean0.06976744186
Median Absolute Deviation (MAD)0
Skewness3.500951685
Sum3
Variance0.06644518272
MonotonicityNot monotonic
2022-11-25T21:28:59.370277image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
040
93.0%
13
 
7.0%
ValueCountFrequency (%)
040
93.0%
13
 
7.0%
ValueCountFrequency (%)
13
 
7.0%
040
93.0%

Sleep
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1395348837
Minimum0
Maximum1
Zeros37
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size387.0 B
2022-11-25T21:28:59.490344image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3506046036
Coefficient of variation (CV)2.512666326
Kurtosis2.777851022
Mean0.1395348837
Median Absolute Deviation (MAD)0
Skewness2.156553432
Sum6
Variance0.122923588
MonotonicityNot monotonic
2022-11-25T21:28:59.600093image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
037
86.0%
16
 
14.0%
ValueCountFrequency (%)
037
86.0%
16
 
14.0%
ValueCountFrequency (%)
16
 
14.0%
037
86.0%

CaffeineCups
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)6.2%
Missing11
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean0.125
Minimum0
Maximum1
Zeros28
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:28:59.715535image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3360107525
Coefficient of variation (CV)2.68808602
Kurtosis3.909359606
Mean0.125
Median Absolute Deviation (MAD)0
Skewness2.380876189
Sum4
Variance0.1129032258
MonotonicityNot monotonic
2022-11-25T21:28:59.820471image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
028
65.1%
14
 
9.3%
(Missing)11
 
25.6%
ValueCountFrequency (%)
028
65.1%
14
 
9.3%
ValueCountFrequency (%)
14
 
9.3%
028
65.1%

AlcoholDrinks
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct4
Distinct (%)12.5%
Missing11
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean0.21875
Minimum0
Maximum3
Zeros28
Zeros (%)65.1%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:28:59.930585image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.45
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.659147788
Coefficient of variation (CV)3.013247031
Kurtosis11.35329182
Mean0.21875
Median Absolute Deviation (MAD)0
Skewness3.343274964
Sum7
Variance0.4344758065
MonotonicityNot monotonic
2022-11-25T21:29:00.035418image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
028
65.1%
12
 
4.7%
31
 
2.3%
21
 
2.3%
(Missing)11
 
25.6%
ValueCountFrequency (%)
028
65.1%
12
 
4.7%
21
 
2.3%
31
 
2.3%
ValueCountFrequency (%)
31
 
2.3%
21
 
2.3%
12
 
4.7%
028
65.1%

Stress
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct15
Distinct (%)46.9%
Missing11
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean0.27125
Minimum0
Maximum0.84
Zeros2
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:29:00.165390image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.022
Q10.16
median0.24
Q30.36
95-th percentile0.574
Maximum0.84
Range0.84
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1836502637
Coefficient of variation (CV)0.6770516634
Kurtosis1.843700082
Mean0.27125
Median Absolute Deviation (MAD)0.08
Skewness1.129214894
Sum8.68
Variance0.03372741935
MonotonicityNot monotonic
2022-11-25T21:29:00.290662image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.166
14.0%
0.246
14.0%
0.522
 
4.7%
02
 
4.7%
0.322
 
4.7%
0.282
 
4.7%
0.442
 
4.7%
0.122
 
4.7%
0.362
 
4.7%
0.641
 
2.3%
Other values (5)5
11.6%
(Missing)11
25.6%
ValueCountFrequency (%)
02
 
4.7%
0.041
 
2.3%
0.081
 
2.3%
0.122
 
4.7%
0.166
14.0%
0.21
 
2.3%
0.246
14.0%
0.282
 
4.7%
0.322
 
4.7%
0.362
 
4.7%
ValueCountFrequency (%)
0.841
 
2.3%
0.641
 
2.3%
0.522
 
4.7%
0.442
 
4.7%
0.41
 
2.3%
0.362
 
4.7%
0.322
 
4.7%
0.282
 
4.7%
0.246
14.0%
0.21
 
2.3%

Sleepiness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)50.0%
Missing11
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean0.49625
Minimum0.16
Maximum0.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:29:00.415493image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.182
Q10.4
median0.46
Q30.6
95-th percentile0.814
Maximum0.88
Range0.72
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1819916694
Coefficient of variation (CV)0.3667338427
Kurtosis-0.05704363575
Mean0.49625
Median Absolute Deviation (MAD)0.1
Skewness0.1960976692
Sum15.88
Variance0.03312096774
MonotonicityNot monotonic
2022-11-25T21:29:00.540104image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.445
11.6%
0.565
11.6%
0.44
 
9.3%
0.162
 
4.7%
0.682
 
4.7%
0.62
 
4.7%
0.362
 
4.7%
0.882
 
4.7%
0.481
 
2.3%
0.721
 
2.3%
Other values (6)6
14.0%
(Missing)11
25.6%
ValueCountFrequency (%)
0.162
 
4.7%
0.21
 
2.3%
0.281
 
2.3%
0.321
 
2.3%
0.362
 
4.7%
0.44
9.3%
0.445
11.6%
0.481
 
2.3%
0.521
 
2.3%
0.565
11.6%
ValueCountFrequency (%)
0.882
 
4.7%
0.761
 
2.3%
0.721
 
2.3%
0.682
 
4.7%
0.641
 
2.3%
0.62
 
4.7%
0.565
11.6%
0.521
 
2.3%
0.481
 
2.3%
0.445
11.6%

PreviousNightMood
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)65.2%
Missing20
Missing (%)46.5%
Infinite0
Infinite (%)0.0%
Mean15.91304348
Minimum8
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:29:00.675235image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9.2
Q113
median15
Q318.5
95-th percentile23.9
Maximum24
Range16
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.601512039
Coefficient of variation (CV)0.2891660571
Kurtosis-0.6499156583
Mean15.91304348
Median Absolute Deviation (MAD)3
Skewness0.3094022038
Sum366
Variance21.17391304
MonotonicityNot monotonic
2022-11-25T21:29:00.795448image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
153
 
7.0%
133
 
7.0%
112
 
4.7%
242
 
4.7%
172
 
4.7%
162
 
4.7%
81
 
2.3%
181
 
2.3%
121
 
2.3%
191
 
2.3%
Other values (5)5
 
11.6%
(Missing)20
46.5%
ValueCountFrequency (%)
81
 
2.3%
91
 
2.3%
112
4.7%
121
 
2.3%
133
7.0%
141
 
2.3%
153
7.0%
162
4.7%
172
4.7%
181
 
2.3%
ValueCountFrequency (%)
242
4.7%
231
 
2.3%
221
 
2.3%
211
 
2.3%
191
 
2.3%
181
 
2.3%
172
4.7%
162
4.7%
153
7.0%
141
 
2.3%

Stress7DayRollingMean
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct28
Distinct (%)87.5%
Missing11
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean0.2947886905
Minimum0.2171428571
Maximum0.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:29:00.950203image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.2171428571
5-th percentile0.2247142857
Q10.2557142857
median0.2914285714
Q30.336
95-th percentile0.3823
Maximum0.43
Range0.2128571429
Interquartile range (IQR)0.08028571429

Descriptive statistics

Standard deviation0.05291622186
Coefficient of variation (CV)0.1795056037
Kurtosis-0.05608070989
Mean0.2947886905
Median Absolute Deviation (MAD)0.04228571429
Skewness0.6109719092
Sum9.433238095
Variance0.002800126536
MonotonicityNot monotonic
2022-11-25T21:29:01.085413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.3362
 
4.7%
0.26857142862
 
4.7%
0.29142857142
 
4.7%
0.30666666672
 
4.7%
0.25714285711
 
2.3%
0.23428571431
 
2.3%
0.28666666671
 
2.3%
0.31
 
2.3%
0.26666666671
 
2.3%
0.221
 
2.3%
Other values (18)18
41.9%
(Missing)11
25.6%
ValueCountFrequency (%)
0.21714285711
2.3%
0.221
2.3%
0.22857142861
2.3%
0.231
2.3%
0.23428571431
2.3%
0.241
2.3%
0.24666666671
2.3%
0.25142857141
2.3%
0.25714285711
2.3%
0.2641
2.3%
ValueCountFrequency (%)
0.431
2.3%
0.391
2.3%
0.3761
2.3%
0.361
2.3%
0.35333333331
2.3%
0.351
2.3%
0.34666666671
2.3%
0.3362
4.7%
0.30666666672
4.7%
0.31
2.3%

Sleepiness7DayRollingMean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)96.9%
Missing11
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean0.483139881
Minimum0.3666666667
Maximum0.6571428571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:29:01.230431image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.3666666667
5-th percentile0.3707857143
Q10.400952381
median0.4564285714
Q30.5692857143
95-th percentile0.6345142857
Maximum0.6571428571
Range0.2904761905
Interquartile range (IQR)0.1683333333

Descriptive statistics

Standard deviation0.09364009741
Coefficient of variation (CV)0.1938157066
Kurtosis-1.205218875
Mean0.483139881
Median Absolute Deviation (MAD)0.07442857143
Skewness0.4198872044
Sum15.46047619
Variance0.008768467843
MonotonicityNot monotonic
2022-11-25T21:29:01.380648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.38666666672
 
4.7%
0.50857142861
 
2.3%
0.65714285711
 
2.3%
0.61142857141
 
2.3%
0.56666666671
 
2.3%
0.61
 
2.3%
0.55333333331
 
2.3%
0.51
 
2.3%
0.4481
 
2.3%
0.451
 
2.3%
Other values (21)21
48.8%
(Missing)11
25.6%
ValueCountFrequency (%)
0.36666666671
2.3%
0.371
2.3%
0.37142857141
2.3%
0.37333333331
2.3%
0.381
2.3%
0.3841
2.3%
0.38666666672
4.7%
0.40571428571
2.3%
0.4161
2.3%
0.4241
2.3%
ValueCountFrequency (%)
0.65714285711
2.3%
0.65714285711
2.3%
0.6161
2.3%
0.61142857141
2.3%
0.61
2.3%
0.591
2.3%
0.581
2.3%
0.57714285711
2.3%
0.56666666671
2.3%
0.56571428571
2.3%

AtHome
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)4.8%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.1666666667
Minimum0
Maximum1
Zeros35
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size688.0 B
2022-11-25T21:29:01.516334image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3771954702
Coefficient of variation (CV)2.263172821
Kurtosis1.513846154
Mean0.1666666667
Median Absolute Deviation (MAD)0
Skewness1.855801714
Sum7
Variance0.1422764228
MonotonicityNot monotonic
2022-11-25T21:29:01.632902image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
035
81.4%
17
 
16.3%
(Missing)1
 
2.3%
ValueCountFrequency (%)
035
81.4%
17
 
16.3%
ValueCountFrequency (%)
17
 
16.3%
035
81.4%

Interactions

2022-11-25T21:28:49.643929image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:27:40.353176image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:27:43.757555image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:27:47.010640image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:27:51.010468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:27:54.076030image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:27:57.215338image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:00.529501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:03.690595image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:06.770342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:09.970158image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:12.780270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:15.655600image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:18.610362image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:21.843875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:24.670461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T21:28:27.630160image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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Correlations

2022-11-25T21:29:01.810092image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-25T21:29:02.170194image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-25T21:29:02.539350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-25T21:29:02.860234image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-25T21:29:03.050064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-25T21:28:52.795266image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-25T21:28:53.480559image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-25T21:28:53.790272image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-25T21:28:54.029395image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateMoodMood_numMood_binMood_bin_numTimeDayEntertainmentExerciseFamilyFoodFriendsHobbyLoveNightOutProjectsSchoolSelfCareSleepCaffeineCupsAlcoholDrinksStressSleepinessPreviousNightMoodStress7DayRollingMeanSleepiness7DayRollingMeanAtHome
02022-09-30Good3Good116.100000Friday0101100001000.00.00.160.16NaNNaNNaN1.0
12022-10-01Okay2Bad016.200000Saturday0000000001000.00.00.240.44NaNNaNNaN1.0
22022-10-02Bad1Bad020.750000Sunday0010000000000.00.00.520.56NaNNaNNaN1.0
32022-10-03Terrific4Good118.350000Monday0111111011110.00.00.240.32NaN0.2900000.3700000.0
42022-10-04Terrific4Good122.733333Tuesday0000001000100.01.00.160.44NaN0.2640000.3840000.0
52022-10-05Okay2Bad022.150000Wednesday0000000001000.00.00.520.40NaN0.3066670.3866670.0
62022-10-06Terrible0Bad016.350000Thursday000000000100NaNNaNNaNNaNNaN0.3066670.3866670.0
72022-10-07Good3Good117.316667Friday001010000100NaNNaNNaNNaNNaN0.3360000.4320000.0
82022-10-08Good3Good118.866667Saturday0010000000101.00.00.240.40NaN0.3360000.4240001.0
92022-10-09Bad1Bad022.916667Sunday1000001000000.00.00.640.64NaN0.3600000.4400000.0

Last rows

DateMoodMood_numMood_binMood_bin_numTimeDayEntertainmentExerciseFamilyFoodFriendsHobbyLoveNightOutProjectsSchoolSelfCareSleepCaffeineCupsAlcoholDrinksStressSleepinessPreviousNightMoodStress7DayRollingMeanSleepiness7DayRollingMeanAtHome
332022-11-02Okay2Bad016.816667Wednesday0000000001000.00.00.440.56NaN0.2300000.4500000.0
342022-11-03Terrific4Good118.833333Thursday0110101001001.00.00.280.4416.00.2400000.4480000.0
352022-11-04Good3Good116.000000Friday0110101001000.02.00.120.7611.00.2200000.5000000.0
362022-11-05Terrible0Bad013.533333Saturday0000001000000.00.00.360.6822.00.2666670.5533331.0
372022-11-06Okay2Bad016.000000Sunday0001001000000.00.00.360.569.00.3000000.6000000.0
382022-11-07Okay2Bad016.400000Monday0000000001010.00.00.160.4014.00.2866670.5666670.0
392022-11-08Good3Good116.116667Tuesday0000100011000.00.00.320.8821.00.2914290.6114290.0
402022-11-09Terrific4Good121.750000Wednesday0100100011000.00.00.040.8815.00.2342860.6571430.0
412022-11-10Good3Good117.783333Thursday0000100010000.01.00.160.4424.00.2171430.6571430.0
422022-11-11Good3Good116.550000Friday000000100000NaNNaNNaNNaNNaNNaNNaNNaN